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Processes
Special Issues
Design and Analysis of Adaptive Identification and Control
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Design and Analysis of Adaptive Identification and Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3418

Special Issue Editors

Dr. Weicun Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; self-tuning control; multiple model adaptive control; multiple model adaptive estimation; stability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor
School of Engineering, University of the West of England-Frenchy Campus, Coldharbour Lane, Bristol, UK
Interests: dynamic system modeling; identification; control; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive control originated from the gain scheduling control of high-performance aircraft in the early 1950s. To be specific, model reference adaptive control (MRAC) was proposed by Whitaker et al. to solve the control problem of an autopilot. From the viewpoint of theory research, self-tuning control (STC) was proposed by Kalman in 1958 to deal with the optimal control of a stochastic system with unknown or time-varying parameters and then connected with actual applications in paper-making machine through the pioneering work of Astrom and Wittenmark. It is well known that identification is the most important component of an adaptive control system.

This Special Issue will explore recent technological developments in adaptive identification and control (design methods and theoretical analysis), especially for nonlinear stochastic processes such as robotic systems, manufacturing systems, transportation systems, power systems, chemical systems, etc.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  • Identification and self-tuning adaptive control;
  • Event-triggered adaptive identification and control;
  • Intelligent adaptive control;
  • Robust adaptive control;
  • Adaptive sliding-mode control.

Dr. Weicun Zhang
Prof. Dr. Quanmin Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adaptive identification and control systems
  • design and analysis
  • adaptive identification and control system simulation
  • process modeling/identification
  • applications of adaptive identification and control system
  • stability and convergence of adaptive identification and control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

14 pages, 2536 KiB  
Article
Optimization of Weighted Geometrical Center Method for PI and PI-PD Controllers
by Mahmut Daskin
Processes 2025, 13(3), 749; https://doi.org/10.3390/pr13030749 - 4 Mar 2025
Abstract
This study proposes an optimized approach to enhance the performance of the Weighted Geometric Center (WGC) method for stabilizing time-delay systems, which has applications in industrial process control, robotics, and high-order dynamic systems. The traditional WGC method determines controller parameters by calculating the [...] Read more.
This study proposes an optimized approach to enhance the performance of the Weighted Geometric Center (WGC) method for stabilizing time-delay systems, which has applications in industrial process control, robotics, and high-order dynamic systems. The traditional WGC method determines controller parameters by calculating the Weighted Geometric Center of the stable region, but it often overlooks better-performing parameter pairs near the WGC point. To address this limitation, a goal function is formulated based on percentage overshoot, rise time, and settling time. The optimization process explores the vicinity of the WGC and selects controller parameters that minimize the goal function, ensuring improved performance. The proposed optimization is applied to PI and PI-PD controllers, and its effectiveness is demonstrated through multiple case studies. Simulation results indicate that the optimized method significantly improves control performance, particularly in reducing overshoot, enhancing settling time, and ensuring a more stable response compared to the conventional WGC method. For instance, the Optimized WGC method reduces overshoot by up to 15% and settling time by up to 20%. These findings highlight the practical benefits of integrating local optimization into the WGC framework for superior controller tuning in time-delay systems. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
►▼ Show Figures

Figure 1

Figure 1
<p>Stability region of this system can be obtained by using stability boundary locus approach [<a href="#B30-processes-13-00749" class="html-bibr">30</a>].</p> Full article ">Figure 2
<p>Stability boundaries for the given system.</p> Full article ">Figure 3
<p>WGC point of stability region.</p> Full article ">Figure 4
<p>Flowchart of WGC optimization process.</p> Full article ">Figure 5
<p>Rectangular area around WGC point and Optimized WGC point.</p> Full article ">Figure 6
<p>The rectangular area around the WGC point.</p> Full article ">Figure 7
<p>Step responses for Example 1.</p> Full article ">Figure 8
<p>Robustness comparison using Kharitonov functions. (<b>a</b>) Response for Equation (24), (<b>b</b>) Response for Equation (25), (<b>c</b>) Response for Equation (26), and (<b>d</b>) Response for Equation (27).</p> Full article ">Figure 9
<p>Block diagram of PI-PD-controlled system.</p> Full article ">Figure 10
<p>Variable step responses for Example 2.</p> Full article ">Figure 11
<p>Block diagram of PI-controlled system.</p> Full article ">Figure 12
<p>Step responses for Example 3.</p> Full article ">
27 pages, 5279 KiB  
Article
Research on Unmanned Aerial Vehicle Intelligent Maneuvering Method Based on Hierarchical Proximal Policy Optimization
by Yao Wang, Yi Jiang, Huiqi Xu, Chuanliang Xiao and Ke Zhao
Processes 2025, 13(2), 357; https://doi.org/10.3390/pr13020357 - 27 Jan 2025
Viewed by 583
Abstract
Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current [...] Read more.
Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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Figure 1

Figure 1
<p>The UAV motion model.</p> Full article ">Figure 2
<p>Training process of PPO.</p> Full article ">Figure 3
<p>Network structure diagram.</p> Full article ">Figure 4
<p>Framework of H-PPO.</p> Full article ">Figure 5
<p>Diagram of UAV detection areas.</p> Full article ">Figure 6
<p>UAV detection model.</p> Full article ">Figure 7
<p>A schematic diagram of the experimental task area.</p> Full article ">Figure 8
<p>Reward function curves for different learning rates.</p> Full article ">Figure 9
<p>Maneuver reward curve in training phase (5 obstacles).</p> Full article ">Figure 10
<p>Maneuver reward curve in training phase (10 obstacles).</p> Full article ">Figure 11
<p>Maneuver reward curve in training phase (15 obstacles).</p> Full article ">Figure 12
<p>Number of movable threat targets and success rate.</p> Full article ">Figure 13
<p>Intelligent agent maneuver test diagram.</p> Full article ">Figure 14
<p>Dynamic scene test diagram.</p> Full article ">Figure 15
<p>Relationship between success rate and maximum maneuvering speed.</p> Full article ">
21 pages, 28395 KiB  
Article
Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer
by Liangtong Shi, Minghao Lv and Pengwei Li
Processes 2024, 12(11), 2581; https://doi.org/10.3390/pr12112581 - 18 Nov 2024
Viewed by 999
Abstract
To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode [...] Read more.
To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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Figure 1

Figure 1
<p>Trajectory diagram of super-twisting algorithm.</p> Full article ">Figure 2
<p>Schematic diagram of super-twisting sliding mode observer.</p> Full article ">Figure 3
<p>Comparison of LSTA and STA diagrams.</p> Full article ">Figure 4
<p>Phase-Locked Loop Diagram.</p> Full article ">Figure 5
<p>TESO-PLL schematic diagram.</p> Full article ">Figure 6
<p>TESO structure diagram.</p> Full article ">Figure 7
<p>Block diagram of the overall implementation of the improved adaptive SMO.</p> Full article ">Figure 8
<p>Block diagram of permanent magnet synchronous motor control system based on VGLSTA-SMO.</p> Full article ">Figure 9
<p>Comparison of rotational speed errors between LSTA and STA observations.</p> Full article ">Figure 10
<p>STA-SMO estimated and actual rotor position waveforms under different speeds: (<b>a</b>) 4000 rpm; (<b>b</b>) 1000 rpm.</p> Full article ">Figure 11
<p>LSTA-SMO estimated and actual rotor position waveforms under different speeds: (<b>a</b>) 4000 rpm; (<b>b</b>) 1000 rpm.</p> Full article ">Figure 12
<p>Comparison of LSTA-SMO and STA-SMO Observation Angle Errors.</p> Full article ">Figure 13
<p>Comparison of LSTA-SMO observation angle errors for different slip film coefficients: (<b>a</b>) LSTA-SMO observation angle error when Z1 = 250, and Z2 = 500; (<b>b</b>) LSTA-SMO observation angle error when Z1 = 450, and Z2 = 900.</p> Full article ">Figure 14
<p>Comparison of LSTA-SMO observation speed errors for different slip film coefficients: (<b>a</b>) LSTA-SMO observation angle error when Z = 250, and Z2 = 500; (<b>b</b>) LSTA-SMO observation angle error when Z1 = 450, and Z2 = 900.</p> Full article ">Figure 15
<p>Matlab simulation structure of VGLSTA-SMO.</p> Full article ">Figure 16
<p>Proposed VGLSTA-SMO sliding mode gain variation (<b>a</b>) Z1 value; (<b>b</b>) Z2 value.</p> Full article ">Figure 17
<p>Plot of rotational speed observations under constant sliding mode gain control: (<b>a</b>) comparison of observed RPM, actual RPM, and given RPM; (<b>b</b>) local enlargement.</p> Full article ">Figure 18
<p>Plot of rotational speed observations under adaptive variable smooth mode gain control: (<b>a</b>) comparison of observed RPM, actual RPM, and given RPM; (<b>b</b>) local enlargement.</p> Full article ">Figure 19
<p>Plot of motor speed error observed for LSTA-SMO with constant slip film gain vs. VGLSTA-SMO with variable slip film gain: (<b>a</b>) Speed Error Comparison; (<b>b</b>) local enlargement.</p> Full article ">Figure 20
<p>Plot of actual speed observed rotational speed for a given speed of 10,000 rpm with variations due to load torque: (<b>a</b>) speed observed under adaptive variable smoothing mode gain VGLSTA-SMO control; (<b>b</b>) speed observed under constant gain LSTA-SMO control.</p> Full article ">Figure 21
<p>Comparison of LSTA-SMO observation speed errors for different slip film coefficients: (<b>a</b>) comparison of observed and actual values of position angle; (<b>b</b>) local enlargement.</p> Full article ">Figure 22
<p>Constant Slip Coefficient LSTA-SMO Position Angle Observations: (<b>a</b>) comparison of observed and actual values of position angle; (<b>b</b>) local enlargement.</p> Full article ">Figure 23
<p>Comparison of motor Position Angle Error Observation of LSTA-SMO and VGLSTA-SMO.</p> Full article ">
20 pages, 1758 KiB  
Article
Research on the Identification Method of Respiratory Characteristic Parameters during Mechanical Ventilation
by Yuxin Zhang, Jing Bai, Xingyi Ma and Yu Xu
Processes 2024, 12(8), 1719; https://doi.org/10.3390/pr12081719 - 15 Aug 2024
Viewed by 637
Abstract
In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In [...] Read more.
In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In this method, the patient’s respiratory sample set is obtained through non-invasive measurements. Experimental results demonstrate that the mean relative error of pulmonary elastance identification was 14.25%, and the mean relative error of intrapulmonary pressure identification was 12.33% using the Romberg integral algorithm. For chronic patients using non-invasive ventilators, the fault-tolerant space for ventilator parameter setting is large; this method meets the requirement of auxiliary setting of non-invasive ventilator parameters. For invasive ventilators, a respiratory characteristic parameter identification method based on the AVOV–BP neural network is established. In this method, the patient’s respiratory sample set is obtained through real-time invasive measurements. Even with small sample datasets, experimental results show that the mean relative error of pulmonary elastance identification and intrapulmonary pressure identification were both 0.22%. For critically ill patients using invasive ventilators, the fault-tolerant space for ventilator parameter setting is small; this method meets the requirement of auxiliary setting of invasive ventilator parameters. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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Figure 1

Figure 1
<p>Simulation waveform curves of the TS respiration model.</p> Full article ">Figure 2
<p>Fitting curves of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>E</mi> </msub> <mo stretchy="false">(</mo> <mi>V</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> by four numerical integration algorithms.</p> Full article ">Figure 3
<p>Fitting curves of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> by four numerical integration algorithms.</p> Full article ">Figure 4
<p>Fitting curves of <math display="inline"><semantics> <mrow> <mi>V</mi> <mo>−</mo> <msub> <mi>P</mi> <mi>A</mi> </msub> </mrow> </semantics></math> by four numerical integration algorithms.</p> Full article ">Figure 5
<p>Measured respiratory data of one respiratory cycle.</p> Full article ">
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